Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site “A” for DST implementation and site “B” for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge

GIS modeling of firebase of urban gas distribution networks and seismic effects in its intensification (Case study: District 1 of Tabriz Municipality)

Today, about 60% of the world's energy resources are oil and gas. Due to the different methods of transporting crude oil and its products, the largest share of the transfer of these resources is through the transmission pipeline lines. The present study aims to model the GIS model of fire-based urban gas distribution networks and the seismic effects of Tabriz in intensifying fire. For this purpose, multi-criteria decision-making methods (MCDA) with geographic information systems (GIS) were used. Also, to determine the importance of the relationship between criteria and sub-criteria and their relative importance coefficient, the FANP model was used. And 20 sub-criteria were studied to study the vulnerability to gas network fires. To determine the effect of seismicity of Tabriz city on fire in urban gas distribution networks, the seismic hazard zoning map of Tabriz city was compared with the output map of the present study and it was determined that the most vulnerability in both seismic hazard maps and fire zoning map The gas network is in the northern and northwestern part of the area, which is a worn and marginal part of the city. Residential use with 70.63 hectares with the most damage from the fire of urban gas distribution networks due to earthquake intensification is in the first place. Considering the high risk of fire in urban gas networks, in the region, especially the worn-out and marginal structures, it is necessary to organize these structures and carry out protective operations of gas transmission lines in the mentioned issues. Also, according to the results of the research, the complexity and length of gas transmission lines in the suburban fabric of the city are high, so it is recommended to use polyethylene pipes in these areas, which have a high resistance to steel pipes

Landslide detection using multi-scale image segmentation and different machine learning models in the higher Himalayas

Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success

An evidential reasoning geospatial approach to transport corridor susceptibility zonation

PhD ThesisGiven the increased hazards faced by transport corridors such as climate induced extreme weather, it is essential that local spatial hot-spots of potential landslide susceptibility can be recognised. Traditionally, geotechnical survey and monitoring approaches have been used to recognise spatially landslide susceptibility zones. The increased availability of affordable very high resolution remotely-sensed datasets, such as airborne laser scanning (ALS) and multispectral aerial imagery, along with improved geospatial digital map data-sets, potentially allows the automated recognition of vulnerable earthwork slopes. However, the challenge remains to develop the analytical framework that allows such data to be integrated in an objective manner to recognise slopes potentially susceptible to failure. In this research, an evidential reasoning multi-source geospatial integration approach for the broad-scale recognition and prediction of landslide susceptibility in transport corridors has been developed. Airborne laser scanning and Ordnance Survey DTM data is used to derive slope stability parameters (slope gradient, aspect, terrain wetness index (TWI), stream power index (SPI) and curvature), while Compact Airborne Spectrographic Imager (CASI) imagery, and existing national scale digital map data-sets are used to characterise the spatial variability of land cover, land use and soil type. A novel approach to characterisation of soil moisture distribution within transport corridors is developed that incorporates the effects of the catchment contribution to local zones of moisture concentration in earthworks. In this approach, the land cover and soil type of the wider catchment are used to estimate the spatial contribution of precipitation contributing to surface runoff, which in turn is used to parameterise a weighted terrain accumulation flow model. The derived topographic and land use properties of the transport corridor are integrated within the evidential reasoning approach to characterise numeric measures of belief, disbelief and uncertainty regarding slope instability spatially within the transport corridor. Evidential reasoning was employed as it offers the ability to derive an objective weighting of the relative importance of each derived property to the final estimation of landslide susceptibility, whilst allowing the uncertainty of the properties to be taken into account. The developed framework was applied to railway transport earthworks located near Haltwhistle in northern England, UK. This section of the Carlisle-Newcastle rail line has a ii history of instability with the occurrence of numerous minor landslides in recent years. Results on spatial distribution of soil moisture indicate considerable contribution of the surrounding wider catchment topography to the localised zones of moisture accumulation. The degrees of belief and disbelief indicated the importance of slope with gradients between 250 to 350 and concave curvature. Permeable soils with variable intercalations accounted for over 80% of slope instability with 5.1% of the earthwork cuttings identified as relatively unstable in contrast to 47.5% for the earthwork embankment. The developed approach was found to have a goodness of fit of 88.5% with respect to the failed slopes used to parametrise the evidential reasoning model and an overall predictive capability of 77.75% based on independent validation dataset.TETFUND Nigeria, Nasarawa State University and my family members for their financial support towards the completion of the PhD programme

Caractérisation lithologique par fusion évidentielle de résultats de démixage par ratio de bandes voisines et de données géochimiques

L’activité minière fait partie des principales sources de matières premières utilisées en industrie. Elle constitue un des piliers indispensables à l’activité économique au Canada et dans le monde. Cet apport de matière première est cependant dépendant de gisements qui doivent être découverts et caractérisés avant leur exploitation. Dans le secteur minier, la télédétection optique fait partie intégrante des données utilisées en exploration. Parmi les outils disponibles, les méthodes de démixage visent à fournir des informations quantitatives sur les matériaux présents dans la scène observée par le capteur. Les analyses s’appuient sur des modèles théoriques de mélange spectral pour démixer les spectres de réflectance de surfaces à plusieurs composants. Presque exclusivement utilisées en exploration, le démixage est généralement appliqué sur des images satellitaires et aéroportées. Dans cette thèse, il est envisagé comme source d’information complémentaire pour la caractérisation lithologique de mines à ciel ouvert en phase d’exploitation. Il est combiné par fusion évidentielle à l’information géochimique tirée d’analyses d’échantillons. Le site d’étude de cette thèse est la mine d’or à ciel ouvert Canadian Malartic, située dans la province canadienne du Québec, en Abitibi. La mine utilise des méthodes conventionnelles d’exploitation avec forage, dynamitage et transport par tombereau. Ce contexte particulier entraîne cependant d’importantes difficultés dans l’application du démixage. L’activité frénétique d’une mine en exploitation produit en effet une quantité considérable de poussière, masquant les roches en place. En outre, la compagnie arrose en permanence les chemins empruntés par les véhicules afin de réduire la poussière qu’ils génèrent. Or, les variations d’humidité qui en résultent peuvent impacter les résultats du démixage. Cette thèse aborde donc également ces difficultés. La première étape visait à évaluer les performances de cinq modèles de démixage existants ainsi que d’une nouvelle approche, appelée Neighbor-Band Ratio Unmixing (NBRU). La première expérience a consisté à démixer des spectres de 94 mélanges de minéraux afin d’évaluer leur aptitude à retrouver leurs abondances. Ce premier volet a ainsi mis en évidence l’avantage des modèles de transfert radiatif (MTR) de Hapke et de Shkuratov. Parmi les quatre modèles restants, NBRU s’est démarqué en fournissant les meilleures estimations pour 16 des 94 mélanges testés. Ses erreurs d’estimation moyenne et médiane, tout mélange confondu, étaient de 9,8 et 7,4 %, respectivement. Les modèles ont ensuite été testés sur une image hyperspectral AVIRIS de Cuprite, Nevada, États-Unis. Sans accès à des données additionnelles, les MTR n’ont cependant pu être appliqués. Dans cette seconde étape, NBRU s’est largement distingué en retrouvant le plus fidèlement les distributions spatiales de sept des neufs minéraux cartographiés. Face à l’impossibilité d’application des MTR et aux bons résultats de NBRU, c’est donc ce dernier qui a été retenu dans la suite de la thèse. Pour composer avec le problème d’humidité de la mine, l’approche NBRU a ensuite été modifiée en y intégrant une équation linéaire. Cette fonction exprime l’impact spectral de l’humidité d’après un facteur d’influence prédéfini par calibration, d’un indice d’humidité, et de la longueur d’onde considérée. Elle a été calibrée à partir de deux échantillons collectés dans la mine dont les spectres ont été mesurés à différents niveaux d’humidité. Le démixage des spectres de ces mêmes échantillons a ainsi montré un gain considérable de robustesse face aux variations d’humidité. Les différences d’abondances estimées entre les états saturés et secs restent ainsi en deçà de 4 % tout minéral confondu, contre 10 à plus de 90 % sans l’ajout de la fonction. L’application de l’approche modifiée sur une image Worldview-3 de la mine a cependant abouti à des résultats mitigés. Alors que les abondances de certains minéraux ont paru s’affranchir des variations d’humidité, d’autres, au contraire, ont vu leur sensibilité s’accroître. Ces cartes d’abondance minéralogique ont ensuite été utilisées dans le reste du processus de recherche. Les données géochimiques utilisées dans cette thèse sont des analyses simulées à partir des analyses d’échantillons réels. Ces simulations ont ensuite été interpolées par krigeage universel dans tout l’espace du gisement. Les prédictions ainsi produites ont constitué la deuxième source d’information pour la caractérisation lithologique. Les résultats de démixage et d’interpolation ont dans un premier temps été classifiés par arbre de décision avec ensachement. Plusieurs jeux d’entraînement ont été testés pour les deux sources. Ces classifications ont produit des probabilités d’appartenance pour chaque pixel et pour chacune des quatre classes lithologiques considérées. Ces probabilités ont ensuite été fusionnées d’après la théorie de Dezert-Smarandache (DSmT). Le résultat final est une carte lithologique combinant les informations géochimiques et de démixage. L’amélioration obtenue par addition du démixage s’est cependant avéré limitée, atteignant 6,4 % dans le meilleur des cas.Abstract: Mining activity is one of the main sources of raw material used in the industry and is therefore essential for the economic activity of Canada and around the world. This source of raw materials relies, however, on deposits which must be discovered, explored and exploited. In the mining sector, optical remote sensing takes a key role in the exploration process. Among the methods available, spectral unmixing aims at providing quantitative information on the materials covered by the field of view of the sensor. The analyses are based on spectral mixture models to unmix multi-component reflectance spectra. Almost exclusively used for exploration purposes, spectral unmixing is classically performed on airborne and satellite images. In this thesis, spectral unmixing is used as a complementary source of information to better retrieve the lithological information in an active open pit mine. This source is combined to geochemical analysis using evidential fusion. The study site is the Canadian Malartic Mine. This open pit gold mine is located in Abitibi, in the Canadian province of Québec, between Val-d’Or and Rouyn-Noranda. The mine uses common exploitation methods with drills, blasts and transport by trucks. The site is particularly challenging for unmixing methods. Indeed, mining activities generate a huge amount of dust, which potentially hides in situ rocks and considerably affects the textures of the surfaces. Additionally, the company permanently waters the tracks of the vehicles to reduce the amount of dust produced, which causes important variation of moisture across the mine, with all its spectral consequences. This thesis considers these problems as well. The first experiment compared the results of five existing models as well as those of a new proposed approach called Neighbor-Band Ratio Unmixing (NBRU). Models ability at retrieving mineral abundances was first assessed on 94 spectra of crafted mineral mixtures. This first experiment highlights how radiative transfer models – Hapke’s and Shkuratov’s models – outperform the four remaining one. Among the latter, NBRU obtained the best results with 16 best abundance estimations and mean and median errors of 9.8 et 7.4%, respectively. Models’ robustness was then tested on an AVIRIS hyperspectral image of Cuprite, Nevada, US. However, since insufficient information on the samples of the spectral references were available, transfer radiative models were unworkable. NBRU obtained significantly better results than the three other models tested, retrieving most accurate spatial distributions for seven of the nine minerals mapped. Based on these results, the proposed NBRU approach was selected and applied in the rest of the research. To handle the moisture problem encountered in Canadian Malartic Mine, the NBRU approach was modified by integrating a linear equation. The latter expresses the spectral influence of moisture as a function of a moisture index, an influence factor, and the wavelength considered. The function was calibrated based on spectra of two grinded rock samples with various moisture levels. The unmixing of these spectra showed that the modification of the approach led to a significant improvement of robustness when facing moisture variations. However, its application on a 16-spectral-bands Worldview-3 image of the mine led to arguable results. While the abundance of some minerals appeared to overcome the influence of moisture, other minerals displayed an opposite behavior, showing higher contrasts in its presence. These abundance maps were used as the first source of lithological information in the fusion process. The geochemical data used in this thesis were simulated analysis based on real samples. These virtual samples were interpolated by universal 3D kriging. The predictions were then used as the second source of information in the evidential fusion. The results of the unmixing and of the interpolation steps were then classified using bagged decision trees with various training sets. These classifications led to probabilities for each pixel to belong to the four considered lithological classes. These probabilities were then combined following the Dezert-Smarandache Theory (DSmT). The final result of this research is a lithological map that combines both geochemical and remote sensed information. The fusion of the unmixing results led however to limited gains, improving the precisions of the lithological classifications of 6,4 %, at its best

A land evaluation model for irrigated crops using multi-criteria analysis.

This thesis investigated the optimal land suitability for irrigated crop production of barley and wheat in Benghazi region of Libya using multi-criteria analysis (MCA) of fuzzy logic and the Analytical Hierarchy Process (AHP). In the MCA, fourteen land suitability factors including twelve soil characteristics, topography and erosion hazard were evaluated. Local experts used their experience and assigned different weights based on crop requirements through pairwise comparison matrix. The combination of these methods was aimed at developing existing land evaluation model in the study area that was based on Boolean logic. Three models were developed based on Food and Agriculture Organization Framework: Model 1 was based on existing land evaluation model of Boolean and equal weights; Model 2 was based on Boolean but with difference in weights assigned using AHP; and Model 3 was based on Fuzzy and AHP. The results of these models were compared using crosstab classification (Kappa statistic and overall agreement). On comparison, Model 2 and Model 3 demonstrated higher agreement in spatial distribution of land suitability class than Model 1 for both barley and wheat crops. However, Model 3 is more realistic than the other two models when tested by linear regression. This implies that the application of fuzzy logic and AHP in MCA produces areas that are most suitable for barley and wheat production than would other methods. In practice, however, land management practices by farmers may lead to different yield in the selected suitable area. This thesis makes original contributions in the field of identifying the most suitable land evaluation model for application to crop production improvements. Furthermore, the results of this research will be useful to the Libyan authorities in planning for the optimisation of available land-use for strategic production of barley and wheat crops. This is pertinent to issues of food security. The approaches are transferable to other regions of the world which face similar challenges in domestic food production